Using transfer entropy to measure the patterns of information flow though cortex : application to MEG recordings from a visual Simon task

  • Poster presentation: Functional connectivity of the brain describes the network of correlated activities of different brain areas. However, correlation does not imply causality and most synchronization measures do not distinguish causal and non-causal interactions among remote brain areas, i.e. determine the effective connectivity [1]. Identification of causal interactions in brain networks is fundamental to understanding the processing of information. Attempts at unveiling signs of functional or effective connectivity from non-invasive Magneto-/Electroencephalographic (M/EEG) recordings at the sensor level are hampered by volume conduction leading to correlated sensor signals without the presence of effective connectivity. Here, we make use of the transfer entropy (TE) concept to establish effective connectivity. The formalism of TE has been proposed as a rigorous quantification of the information flow among systems in interaction and is a natural generalization of mutual information [2]. In contrast to Granger causality, TE is a non-linear measure and not influenced by volume conduction. ...

Download full text files

Export metadata

Additional Services

Share in Twitter Search Google Scholar
Metadaten
Author:Michael WibralORCiDGND, Raul VicenteORCiD, Jochen TrieschORCiD, Gordon PipaORCiDGND
URN:urn:nbn:de:hebis:30-70795
DOI:https://doi.org/10.1186/1471-2202-10-S1-P232
Parent Title (English):BMC neuroscience
Document Type:Article
Language:English
Year of Completion:2009
Year of first Publication:2009
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Release Date:2009/09/20
Volume:10(Suppl 1)
Issue:P232
Note:
© 2009 Wibral et al; licensee BioMed Central Ltd.
Source:from Eighteenth Annual Computational Neuroscience Meeting: CNS*2009 Berlin, Germany. 18–23 July 2009
HeBIS-PPN:21898491X
Institutes:Medizin / Medizin
Wissenschaftliche Zentren und koordinierte Programme / Frankfurt Institute for Advanced Studies (FIAS)
Dewey Decimal Classification:5 Naturwissenschaften und Mathematik / 57 Biowissenschaften; Biologie / 570 Biowissenschaften; Biologie
Sammlungen:Sammlung Biologie / Sondersammelgebiets-Volltexte
Licence (German):License LogoDeutsches Urheberrecht